class_docstr: Compute GaussianBlur in 2d.
  
  Makes use of kornia functions, but most notably the kernel is not computed
  during the forward pass, and does not depend on the input size. As a caveat,
  the number of channels that are expected have to be provided during initialization.
docstr: Blur the input with the computed Gaussian.
  
  Args:
      input_tensor (Tensor): Input tensor to be blurred.
  
  Returns:
      Tensor: Blurred output tensor.
docstr_tok: ["Blur","the","input","with","the","computed","Gaussian",".","Args",":","input_tensor","(","Tensor",")",":","Input","tensor","to","be","blurred",".","Returns",":","Tensor",":","Blurred","output","tensor","."]
code: def forward(self, input_tensor: Tensor) -> Tensor:
      """Blur the input with the computed Gaussian.
  
      Args:
          input_tensor (Tensor): Input tensor to be blurred.
  
      Returns:
          Tensor: Blurred output tensor.
      """
      batch, channel, height, width = input_tensor.size()
  
      if self.padding == "same":
          input_tensor = F.pad(
              input_tensor, self.padding_shape, mode=self.border_type
          )
  
      # convolve the tensor with the kernel.
      output = F.conv2d(
          input_tensor,
          self.kernel,
          groups=self.channels,
          padding=0,
          stride=1,
      )
  
      if self.padding == "same":
          out = output.view(batch, channel, height, width)
      else:
          out = output.view(
              batch,
              channel,
              height - self.height + 1,
              width - self.width + 1,
          )
  
      return out
  
file_path: anomalib/src/anomalib/models/components/filters/blur.py
imports: from __future__ import annotations
  from kornia.filters import get_gaussian_kernel2d
  from kornia.filters.filter import _compute_padding
  from kornia.filters.kernels import normalize_kernel2d
  from torch import Tensor, nn
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the GaussianBlur2d obj's underlying member method code with example usage: obj.forward(input_tensor) and returns: out